The authors present a dataset and accompanying paper on airborne lidar of northern Canadian forests.

Due to the inaccessability and size of the area, no wall-to-wall laserscanning is available. Instead, transects have been flown that cover all ecozones of Northern Canada. Lidar metrics are available as full dataset or in an abridged version.

The paper describes the data and gives a lot of background information with an exhaustive literature list. The paper does not provide detailed information on each individual metric, but enough information is given so that the metrics can be understood. 

The paper is very well-written, I could not find any mistakes or major omissions so I recommend publication as is.

The authors present a dataset collected from ALS transects with lidar-derived metrics covering Canada's northern forests. A series of 30 by 30 m lidar metrics was derived to accommodate a wide range of forestry and ecological applications. The manuscript is complete and well-written. However, there are critical aspects missing in order to make the provided datasets useful for a broad community. 

(1) There is no use case demonstrated, illustrating how the provided datasets can be used for "science and application" as the authors stated in the title, such as wildfire fuel mapping, ecosystem monitoring, etc. I think this is an essential aspect of demonstrating how useful this dataset is. 

(2) Similarly, 369 lidar metrics have been calculated and provided; however, it remains unclear to users how to select the relevant/useful/robust metrics for different applications. Concrete examples and suggestions/usage notes should be provided on how to potentially best apply the provided lidar metrics. 

(3) The accuracy of the ALS data collection (horizontal and vertical accuracies of points) and the classification accuracy of the point clouds are not provided or evaluated. This is an important factor to consider in order to know how reliable the datasets and the derived metrics are. Additional evidence needs to be provided.

More detailed comments are as follows:

1. L23. 15 million 900 m2 lidar plots, what is the point density?

2. L25. The authors mentioned that the chosen 30 by 30 m resolution was to match the medium resolution of Landsat and Sentinel 2, still, I believe lidar metrics with finer resolution should be considered, given the pulse/point density of ALS data, and it will be more beneficial for a broader user community.  There are already country-wide lidar metrics derived from ALS data at 10 m resolution (see references below). 30 by 30 m resolution may be a bit limited in finer scale ecological applications. 

Ref: (1) Assmann, J. J., Moeslund, J. E., Treier, U. A., & Normand, S. (2022). EcoDes-DK15: high-resolution ecological descriptors of vegetation and terrain derived from Denmark's national airborne laser scanning data set. Earth System Science Data, 14(2), 823-844. doi:10.5194/essd-14-823-2022

(2) Shi, Y., Wang, J., & Kissling, W. D. (2025). Multi-temporal high-resolution data products of ecosystem structure derived from country-wide airborne laser scanning surveys of the Netherlands. Earth Syst. Sci. Data, 17(7), 3641-3677. doi:https://doi.org/10.5194/essd-17-3641-2025

3. L43-47. Too long for one sentence. Please rephrase.

4. What is the impact of the ALS transects sampling discussed here on areas beyond Canada? It should also be reflected in the Introduction.

5. L66. It is not clear what "linear samples" mean here.

6. L107-108. Please briefly explain how the current study relate/compare to the existing work mentioned here.

7. Table 1. What are the horizontal and vertical accuracy of the acquisitions? What are the data volumes?

8. L185. Why remain low points noise (class 7) in the process?

9. L204. What is the accuracy of the points classification? For instance, what is the extent of misclassification between ground points and low vegetation points? It is essential for lidar metrics calculation and for deriving further metrics/parameters.

10. L221. Are there any data collected in 2025? As in Figure 1 and Table 2, only 2023 and 2024 were listed as acquisition years. This information should be clarified at the beginning.

11. I think Table 5 can be better presented as a Figure. For instance, a land cover map for each ecozone, showing land cover classes and their areas. Lidar plot area can be overlaid with the land cover map with indications of sampling intensity.

12. L255. How many of those areas have been selected for validation? And what are the validation results? Why is the area of validation (20  by 20 m) different from the original sample area (30 by 30 m)?

13. L259. How do you rasterize the scan angles, and what is the use of the rasterized layer?

14. L263. Is this including the volume of the raw point cloud? Why only 2023?

15. L269. This may link to the classification of ground points and low vegetation points. It would be helpful to know the quality of such classification.

16. Figure 4. Why are there large areas of red color in the water area in the Canopy Cover (the third panel)? Those areas are not shown in the P95 layer, meaning they probably are assigned to the NA value (i.e. no returns observed or masked out as water body)? Then why do those areas occur in the Canopy cover layer, probably having 0 value (which may should be NA)?

17. Figure 5. The y-axis label of 1e6 for the number of lidar plots seems wrong. The purpose of showing this figure is not very well-explained in the caption and in the main text.

18. Figure 7. Please elaborate on the multidecadal NTEMS satellite information products. And what are the main things shown in this figure? Can information such as how many plots were repetitively disturbed by fire be included in the figure?

19. L320. Can a use case be demonstrated here?

20. L331. Here I am missing a bit more concrete suggestions for using the 369 metrics derived. For instance, giving guidance on how to select the most relevant metrics/metric types for different ecological applications (with use cases and examples).